The Hybrid Approach: Combining Statistical Machine Translation, Neural Translation, & Human Expertise
Many people compare neural translation to statistical translation. While many studies have pointed to the fluency of neural machine translation, others highlight the strengths of statistical translation, concluding that the two should be combined for improved quality.
Some have taken the notion of QA assurance further by combining machine translation systems with machine post-editing services. This hybrid machine translation workflow results in superior quality and accounts for rare word misinterpretations.
Recently, TechBullion interviewed Maya Ronen, the Chief Operating albania mobile database Officer of Tomedes, one of the top language service providers specializing in localization with a certification in Machine Post-Editing. She highlighted the need for hybrid systems featuring skilled human translators.
“If there’s no human involved in the process, it causes problems. If no one checks it or reviews it, that’s where you see problems in translation arise. This is why it’s best to have humans involved before and throughout the process,” she said.
The future of SMT is uncertain as NMT continues to gain popularity and show strong results. Some of its guiding principles may prove useful to NMT. However, SMT still has its advantages and may continue to be used in some domains or for specific language pairs. Ultimately, you’ll have to decide which approach fits your needs depending on each project's specific needs and goals.